Aiming at the problem that the unbalanced distribution of data in sleep electroencephalogram(EEG) signals and poor comfort in the process of polysomnography information collection will reduce the model's classification ability, this paper proposed a sleep state recognition method using single-channel EEG signals (WKCNN-LSTM) based on one-dimensional width kernel convolutional neural networks(WKCNN) and long-short-term memory networks (LSTM). Firstly, the wavelet denoising and synthetic minority over-sampling technique-Tomek link (SMOTE-Tomek) algorithm were used to preprocess the original sleep EEG signals. Secondly, one-dimensional sleep EEG signals were used as the input of the model, and WKCNN was used to extract frequency-domain features and suppress high-frequency noise. Then, the LSTM layer was used to learn the time-domain features. Finally, normalized exponential function was used on the full connection layer to realize sleep state. The experimental results showed that the classification accuracy of the one-dimensional WKCNN-LSTM model was 91.80% in this paper, which was better than that of similar studies in recent years, and the model had good generalization ability. This study improved classification accuracy of single-channel sleep EEG signals that can be easily utilized in portable sleep monitoring devices.
The existing automatic sleep staging algorithms have the problems of too many model parameters and long training time, which in turn results in poor sleep staging efficiency. Using a single channel electroencephalogram (EEG) signal, this paper proposed an automatic sleep staging algorithm for stochastic depth residual networks based on transfer learning (TL-SDResNet). Firstly, a total of 30 single-channel (Fpz-Cz) EEG signals from 16 individuals were selected, and after preserving the effective sleep segments, the raw EEG signals were pre-processed using Butterworth filter and continuous wavelet transform to obtain two-dimensional images containing its time-frequency joint features as the input data for the staging model. Then, a ResNet50 pre-trained model trained on a publicly available dataset, the sleep database extension stored in European data format (Sleep-EDFx) was constructed, using a stochastic depth strategy and modifying the output layer to optimize the model structure. Finally, transfer learning was applied to the human sleep process throughout the night. The algorithm in this paper achieved a model staging accuracy of 87.95% after conducting several experiments. Experiments show that TL-SDResNet50 can accomplish fast training of a small amount of EEG data, and the overall effect is better than other staging algorithms and classical algorithms in recent years, which has certain practical value.
Objective To explore the effects of dioscin (Dio) on airway inflammation and microRNA-155 (miR-155)/cyclooxygenase 2 (COX-2)/prostaglandin E2 (PGE2) pathways in asthmatic mice. Methods Seventy mice were randomly divided into control group, model group, inhibitor negative control group (inhibitor-NC group), miR-155 inhibitor group, and Dio group, Dio+miR-155 mimic negative control group (Dio+mimic-NC group), Dio+miR-155 mimic group, with 10 mice in each group. Using house dust mite to induce the preparation of asthma mouse models; enzyme linked immunosorbent assay was used to detect the levels of PGE2, tumor necrosis factor α (TNF-α), cysteyl leukotrienes (CysLTs), cysteyl leukotriene receptor 1 (CysLTR1) and interleukin (IL)-4, IL-5, IL-13 in mouse bronchoalveolar lavage fluid (BALF); hematoxylin-eosin and periodic acid-Schiff staining were used to observe the infiltration of inflammatory cells around the airway and the secretion of mucus by goblet cells; quantitative real-time PCR was used to detect the expression levels of miR-155 and COX-2 mRNA in mouse lung tissue; Western blot was used detect the expression of COX-2 protein in mouse lung tissue. Results MiR-155 inhibitor and Dio could reduce the levels of PGE2, TNF-α, CysLTs, CysLTR1 and IL-4, IL-5, IL-13 in BALF of asthmatic mice, reduce lung tissue inflammatory cell infiltration and goblet cell mucus secretion, and reduce lung tissue miR-155, COX-2 mRNA and protein expression; and miR-155 mimic could significantly weaken the anti-asthma effect of Dio. Conclusion The anti-asthma effect of Dio may be related to the inhibition of miR-155/COX-2/PGE2 pathway to reduce airway inflammation in asthmatic mice.